EAGER: Fairness-Aware Personalized Recommendations


PI: James Caverlee

NSF IIS: 1841138, August 2018 to July 2020 (expected)

Project Goals: The goal of this project is to create effective information curator recommendation models that can be personalized for individual users, while maintaining important fairness properties. The advances in uncovering information curators at scale, reliably connecting users to appropriate curators, and ensuring fairness-preserving properties of such curators are critical for trustworthy information supporting an informed populace. By bringing these research advances, datasets, and toolkits to the wider research community, this project can spur additional advances from complementary efforts by other researchers.

Participants:

Research Challenges: A key challenge for personalized curator recommendation is tackling sparsity while carefully modeling curators in complex, noisy, and heterogeneous environments. Compounding this challenge, most current access to information curators is mediated by centralized platforms (like search engines, social networks, and traditional news media), meaning that personal preferences may not align with the goals of these platforms, leading to potentially biased (or even limited) access to curators. A key question is how to maintain fairness properties in curator recommendation.

Broader Impacts: The successful outcome of this project will lead to research advances that can positively impact existing web and social media platforms, as well as provide a theoretical foundation for future advances in information curation recommendation. The advances in uncovering information curators at scale, reliably connecting users to appropriate curators, and ensuring fairness-preserving properties of such curators are critical for a trustworthy information diet supporting an informed populace. This project will develop new classroom materials, new outreach efforts, and new broadening participation workshops and seminars. Together, these efforts will integrate the new knowledge developed as part of the research plan through investments in undergraduate and graduate students, and through course enhancements and research training.

Current Results: In the first year of the project, our research team made a number of strides in improving recommendation, guided by our proposed research effort. Concretely, we have focused on: 1) new neural models of recommendation; and 2) new fairness-aware approaches for recommendation.

Publications:

This material is based upon work supported by the National Science Foundation under Grant No. 1841138. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Date of Last Update: July 2019